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Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach

Saman Mazaheri Khamaneh, Tong Wu

TL;DR

This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks, integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework that provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats.

Abstract

In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks. The first stage focuses on optimizing economic dispatch under normal operating conditions, aiming to minimize generation costs while maintaining the supply-demand balance. The second stage introduces an adversarial attack model, identifying worst-case scenarios that maximize the system's vulnerability by targeting distributed generation (DG). In the third stage, mitigation strategies are developed using fast-response energy storage systems (ESS) to minimize disruptions caused by these attacks. By integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework, this approach provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats. The approach is validated using the IEEE-33 node distribution system to demonstrate its effectiveness in achieving both cost efficiency and system resilience.

Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach

TL;DR

This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks, integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework that provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats.

Abstract

In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks. The first stage focuses on optimizing economic dispatch under normal operating conditions, aiming to minimize generation costs while maintaining the supply-demand balance. The second stage introduces an adversarial attack model, identifying worst-case scenarios that maximize the system's vulnerability by targeting distributed generation (DG). In the third stage, mitigation strategies are developed using fast-response energy storage systems (ESS) to minimize disruptions caused by these attacks. By integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework, this approach provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats. The approach is validated using the IEEE-33 node distribution system to demonstrate its effectiveness in achieving both cost efficiency and system resilience.

Paper Structure

This paper contains 14 sections, 5 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work and Our Contributions
  4. Problem Formulation and Methodology
  5. Stage 1:OPF under Normal Operating Conditions
  6. Stage 2: Adversarial Attack Assessment (AAA) Model
  7. Stage 3: Attack Mitigation
  8. Experimental Results
  9. Adversarial Attack Analysis
  10. ESS Deployment for Attack Mitigation
  11. System Parameter Analysis
  12. Voltage Magnitude Analysis
  13. Power Flow Analysis
  14. Conclusion

Figures (6)

  • Figure 1: Methodology overview of the proposed tri-level optimization framework.
  • Figure 2: The 33-node DS test system; $\bullet$: nodes; [mycircled,draw=black,fill=white, font=]t11: non-attackable DG nodes; [mycircled,draw=red,fill=white, font=]t11: attackable DG nodes.
  • Figure 3: Attack status over different nodes; Nodes: --- Node 4, $\boldsymbol{\cdots}$ Node 10, -- -- Node 27, -- - -- Node 33, $\boldsymbol{\cdots }$$\boldsymbol{\bigtriangleup}$ Node 18.
  • Figure 4: Storage generation output and their SOC over 24 hours; Storage output nodes: 4, 10, 18, 27, 33. Storage SOC: nodes: --- 4, $\textcolor{storageOrange}{\boldsymbol{\cdot \cdot\cdot} }$ 10, -- -- 18, -- - -- 27, $\textcolor{storageGreen}{\boldsymbol{\cdot \cdot *} }$ 33
  • Figure 5: Voltage magnitudes for nodes across different stages over 24 hours; Nodes: -- - -- Node 6, -- --$\boldsymbol{\bigtriangleup}$ Node 10, -- --$\boldsymbol{\circ}$ Node 24. Boundary limits: $\boldsymbol{\cdots}$ minimum (0.9 pu), $\boldsymbol{\cdots}$ maximum (1.1 pu).
  • ...and 1 more figures